ADH1C Antibody

What is ADH1C and what biological functions does it serve?

ADH1C, also known as ADH3, belongs to the zinc-containing alcohol dehydrogenase family. It functions primarily to metabolize ethanol, retinol and other aliphatic alcohols, hydroxysteroids, and lipid peroxidation products . The protein is encoded by the ADH1C gene, with a calculated molecular weight of approximately 40 kDa, though it's typically observed between 37-45 kDa in experimental conditions . This enzyme plays a crucial role in alcohol metabolism pathways and has increasingly been implicated in various cancer types, including colorectal, liver, and lung cancers .

Which experimental applications are most suitable for ADH1C antibody detection?

The ADH1C antibody has been validated for multiple experimental applications. Western blot analysis is highly effective with recommended dilutions of 1:2000-1:12000 . Immunohistochemistry (IHC) applications have been successfully performed on various tissue types including liver, lung cancer, colon, and kidney tissues with recommended dilutions of 1:500-1:2000 . Immunoprecipitation requires 0.5-4.0 μg of antibody for 1.0-3.0 mg of total protein lysate . Immunofluorescence/Immunocytochemistry (IF/ICC) applications work well with HepG2 cells at dilutions of 1:50-1:500 . For optimal results, each testing system should be individually titrated.

What are the key considerations for ADH1C immunohistochemical staining?

For effective immunohistochemical staining of ADH1C, tissue microarrays or sections should undergo routine dewaxing and rehydration followed by antigen recovery. This is typically performed by microwaving tissues in citric saline at 95°C for 90 seconds . Endogenous peroxidase enzymes should be neutralized with 3% hydrogen peroxide. For tissue permeabilization, 0.1% Triton X-100 is recommended, followed by blocking with 5% bovine serum albumin . Primary antibody incubation should be conducted with a 1:200 dilution of rabbit anti-ADH1C antibody at 4°C overnight, followed by appropriate secondary antibody incubation. Visualization can be achieved using DAB substrate, and quantitative analysis performed using appropriate imaging software .

How does ADH1C function in colorectal cancer progression?

Recent studies have identified ADH1C as a tumor suppressor gene in colorectal cancer (CRC). Expression analyses have demonstrated that ADH1C mRNA and protein levels are significantly lower in CRC cell lines and tumor tissues compared to normal intestinal epithelial cell lines and healthy tissues . Functionally, overexpression of ADH1C inhibits the growth, migration, invasion, and colony formation of CRC cell lines and prevents xenograft tumor growth in mouse models . The inhibitory mechanism operates through the ADH1C/PHGDH/PSAT1/serine metabolic pathway, where ADH1C reduces the expression of phosphoglycerate dehydrogenase (PHGDH) and phosphoserine aminotransferase 1 (PSAT1) in the serine synthesis pathway (SSP), consequently decreasing intracellular serine levels crucial for cancer cell metabolism .

What methodologies are recommended for establishing ADH1C-overexpressing cell lines?

To construct ADH1C-overexpressing CRC cell lines (ADH1C-OE), researchers should obtain pCMV6-ADH1C-Myc-DDK (pADH1C) and pCMV6-Myc-DDK (pNC) vectors . These vectors should be transfected into target cell lines (such as HCT116, SW620, HCT8, and HCT15) for 24 hours, after which cells should be seeded into 10 cm dishes at approximately 1000 cells/dish . Following 24 hours of culture, G418 selection at 1.2 mg/mL should be initiated. The culture medium should be regularly replaced with fresh medium containing 10% FBS and 1.2 mg/mL G418. Stable cell clones exhibiting high ADH1C expression should be selected and maintained in DMEM containing 10% FBS and 1.2 mg/mL G418 . For knockdown studies, siRNA duplexes can be obtained from appropriate vendors and transfected using Lipofectamine .

How do ADH1C genetic variants influence alcohol metabolism and disease susceptibility?

ADH1C polymorphisms significantly affect enzyme activity and alcohol metabolism rates. A well-characterized polymorphism results from the mutation of isoleucine (A) to valine (G) at position 350 in exon 8 (rs698), creating two isoforms: γ1 and γ2 . The γ1 gene (containing isoleucine at position 350) encodes an enzyme with an alcohol metabolism rate 2.5 times greater than the γ2 form (containing valine) . This difference affects acetaldehyde accumulation and produces the "flushing" response, potentially reducing alcohol dependence risk. Individuals with the reduced metabolism γ2 Val.Val form tend to consume more alcohol due to extended ethanol persistence in the bloodstream . Another polymorphism, ADH1C rs1789924 (C>T), has been specifically linked to cancer prognosis, particularly in ESCC patients undergoing adjuvant radiotherapy . Studies combining ADH1B2 with ADH1C1 and CYP2E1 (c1/c1) suggest this genetic combination may confer protection against alcohol use disorder .

What are the optimal PCR-based methodologies for ADH1C genotyping?

For ADH1C genotyping, real-time PCR with specific primers is recommended. For ADH1C, the following primer sequences have been validated: forward 5′-GGACGCACGTGGAAAGGAG-3′ and reverse 5′-GAGCGAAGCAGGTCAAATCC-3′ . PCR reactions should include appropriate housekeeping genes such as β-actin (forward 5′-CATGTACGTTGCTATCCAGGC-3′ and reverse 5′-CTCCTTAATGTCACGCACGAT-3′) for normalization . For SNP detection, Sanger sequencing remains a reliable method, particularly when using formalin-fixed paraffin-embedded tumor samples as demonstrated in studies of ADH1C rs1789924 . Statistical analysis of genotyping data should employ paired t-tests for quantitative data that conform to normal distribution, with results represented as mean ± SD and a P value < 0.05 used as the cut-off criterion for significance .

How should researchers interpret contradictory findings regarding ADH1C polymorphisms across different cancer types?

Studies on ADH1C associations with various cancers have yielded significant but inconsistent results . While ADH1C has been linked to liver carcinoma and lung adenocarcinoma survival, its relationship with other cancers shows variability. For instance, ADH1C is involved in tumor immune cell infiltration and cetuximab resistance in colorectal cancer, yet ADH1B and ALDH2 (but not ADH1C) were associated with increased gastric cancer risk in West Bengal, India . Similarly, ADH1C polymorphisms show no significant association with breast cancer development in Caucasians or esophageal cancer in Chinese populations .

To reconcile these contradictions, researchers should:

• Consider population-specific genetic backgrounds when interpreting results

• Ensure adequate sample sizes with appropriate controls

• Account for environmental factors that may interact with genetic variants

• Employ comprehensive approaches combining transcriptomics, proteomics, metabolomics, and in silico analyses as demonstrated in recent CRC studies

• Validate findings using multiple methodologies (e.g., IHC, western blot, PCR) across different sample types

• Conduct functional studies to elucidate the mechanistic basis of observed associations

What are the optimal antibody dilutions and tissue preparation methods for different ADH1C detection applications?

The optimal antibody dilutions and preparation methods vary by application:

For IHC, tissue samples should undergo dewaxing, rehydration, and antigen recovery (95°C for 90s in citric saline). Neutralize endogenous peroxidase with 3% hydrogen peroxide, permeabilize with 0.1% Triton X-100, and block with 5% bovine serum albumin before antibody incubation .

How can researchers troubleshoot inconsistent ADH1C staining patterns across different tissue types?

When encountering inconsistent ADH1C staining patterns, consider these troubleshooting approaches:

• Optimize antigen retrieval methods: Different tissues may require specific pH conditions. While TE buffer pH 9.0 is suggested as primary choice, citrate buffer pH 6.0 is an effective alternative . Adjust retrieval duration based on tissue type.

• Adjust antibody concentration: Sample-dependent variations may require titration of antibody dilutions. Start with recommended ranges (1:500-1:2000 for IHC) and adjust based on signal-to-noise ratio .

• Consider tissue-specific expression levels: ADH1C expression varies significantly between tissues. Liver tissue typically shows strong expression and serves as a positive control, while expression in other tissues may be lower, requiring more sensitive detection methods .

• Validate with multiple detection methods: When IHC results are ambiguous, confirm with alternative techniques such as western blot or real-time PCR to corroborate protein levels with mRNA expression .

• Include appropriate controls: Always include positive controls (known high-expressing tissues like liver) and negative controls (antibody diluent without primary antibody) in each experiment .

What experimental controls are essential when studying ADH1C in the context of cancer metabolism research?

When investigating ADH1C's role in cancer metabolism, several essential controls should be implemented:

• Positive and negative cell line controls: Include cell lines with documented high (normal intestinal epithelial cells) and low (established CRC cell lines) ADH1C expression levels .

• Vector controls: When overexpressing ADH1C, always include empty vector controls (e.g., pCMV6-Myc-DDK as control for pCMV6-ADH1C-Myc-DDK) to account for vector-induced effects .

• Rescue experiments: When knocking down pathway components (like PHGDH or PSAT1), demonstrate specificity by showing that exogenous serine can rescue the phenotypic effects .

• Metabolic measurements: Include direct measurement of relevant metabolites (such as serine levels) to correlate gene/protein changes with metabolic consequences .

• In vivo validation: Confirm in vitro findings using appropriate animal models, such as xenograft tumor growth in nude mice, to establish physiological relevance .

• Clinical sample validation: Verify findings from cell lines and animal models in patient-derived tissues, using techniques like tissue microarrays containing paired tumor and adjacent normal tissues .